300 papers
Physics — Soc-Ph explores the fascinating intersection where mathematical laws meet human behavior, using the rigorous tools of physics to understand social dynamics. From modeling the spread of ideas and financial markets to analyzing crowd movements and network connections, this field treats societies as complex systems governed by underlying patterns. It bridges the gap between hard science and the social world, offering fresh perspectives on how we interact, organize, and evolve together.
Gist.Science curates the latest preprints from arXiv in this category, ensuring you stay ahead of emerging research. We process every new submission to provide both accessible plain-language summaries for general readers and detailed technical overviews for experts. This dual approach makes cutting-edge discoveries in sociophysics understandable without sacrificing scientific depth. Below are the latest papers from this rapidly growing field, ready for you to explore.
This paper proposes replacing the traditional Periodic Boundary Condition with a Linear eXtrapolation Condition to enable instantaneous spectra analysis of pulse series, a method demonstrated on lung sounds to effectively visualize the time-frequency structures of abnormalities like crackles and wheezing.
This paper presents a method to extend existing age-based social contact matrices by incorporating additional socio-demographic factors using population structure data and mixing assumptions, demonstrating that such stratification significantly alters epidemic dynamics and outcomes, particularly for minority groups.
This paper proposes a graphon-based kinetic framework that models the coupled dynamics of opinion formation, physical contact patterns, and disease transmission to reveal how shaping population opinions can effectively control epidemic spread.
This paper establishes an analytical foundation for adversarial synchronization control in Kuramoto oscillator networks by deriving an exact closed-form expression for the effect of gradient-based perturbations, revealing that finite, coupling-independent increments from repeated kicks explain disproportionate amplification and highlighting fundamental asymmetries between enhancement and suppression.
This paper proposes that engineered aerosol particles with traceable signatures and measurable radiative forcing effects could serve as foundational technical building blocks to enable future multi-state coordination and governance of Stratospheric Aerosol Injection (SAI), while emphasizing that actual deployment remains premature.
The Big Mysteries Survey reveals that many foundational and controversial topics in contemporary physics, often perceived as having broad consensus, are actually supported by much narrower majorities or pluralities among physicists.
This paper argues that constructing a second synchrotron in the Greater Caribbean is a financially feasible and strategic investment that will drive regional development by creating "cities of knowledge" and distributing scientific benefits across multiple nations through a network of smaller accelerators, rather than serving as a mere luxury.
This paper demonstrates that quenched network topology significantly influences the critical behavior of a triplet majority-rule opinion-dynamics model with collective reversal, shifting the order-disorder transition point and altering effective critical exponents, particularly in highly clustered Watts-Strogatz networks where local correlations play a decisive role.
This study analyzes Europe's power sector over 35 historical weather years to conclude that coping with extreme "Dunkelflaute" events in a decarbonized system requires a massive expansion of long-duration electricity storage, amounting to approximately 7% of annual demand, as geographical balancing and fossil-based backups offer limited mitigation.
This paper demonstrates that task complexity fundamentally shapes the internal representations and robustness of neural networks by revealing that hard tasks rely on precise weight magnitudes while easy tasks depend primarily on signed bipartite topology, a distinction quantified by a novel performance gap metric between full-precision and binarized or shuffled models.